The proposed interdisciplinary training program at the University of Minnesota combines graduate training in the computational, chemical, physical, and engineering sciences with graduate training in neuroscience. Neuroscience is a highly interdisciplinary field that uses a variety of experimental approaches to understand the development, structure, and function of the nervous system. As neuroscience matures, the need grows for quantitative modeling, physical and chemical insights, advanced technologies, and state-of-the-art hardware and software that the computational, chemical, physical and engineering sciences can provide. Therefore, interdisciplinary graduate training is needed to take the maximum advantage of these opportunities. The proposal incorporates the strengths, resources, and administrative structures of several existing graduate programs and the University of Minnesota Supercomputing Institute with an interdisciplinary faculty with diverse research interests, to provide a new paradigm in graduate education. We propose to offer 5 three-year Fellowships each year to attract outstanding pre-doctoral students: Enrolling in existing degree programs in Biomedical Engineering, Chemistry, Computer Science, Mathematics, Neuroscience, Physics and Scientific Computation. The Fellows will be trained across disciplines using a variety of tools including special interdisciplinary coursework, research rotations, dual thesis advisors, special seminars and symposia and unique training opportunities. Each trainee's thesis work will cross the disciplines of neuroscience and the physical/computational sciences. An advisory system will help guide students through the program. Also, several mechanisms are proposed to evaluate the effectiveness of the training program. The trainees will receive instructions in the responsible conduct of research. The proposal documents the efforts that the training program and University will make to ensure that traditionally underrepresented students are recruited and included in the program. On completion the trainees will be prepared for research careers in academia, industry and government. The overall goal is to train the next generation of scientists who can bridge the gap between biology and the physical/computational sciences. Broader impacts include advancing our understanding of the brain, cross-fertilization of the disciplines, and establishing a new model for interdisciplinary graduate training.